Search results for "Convolution"
showing 10 items of 334 documents
PNeuro: A scalable energy-efficient programmable hardware accelerator for neural networks
2018
Proceedings of a meeting held 19-23 March 2018, Dresden, Germany; International audience; Artificial intelligence and especially Machine Learning recently gained a lot of interest from the industry. Indeed, new generation of neural networks built with a large number of successive computing layers enables a large amount of new applications and services implemented from smart sensors to data centers. These Deep Neural Networks (DNN) can interpret signals to recognize objects or situations to drive decision processes. However, their integration into embedded systems remains challenging due to their high computing needs. This paper presents PNeuro, a scalable energy-efficient hardware accelerat…
Some experimental issues of AFM tip blind estimation. The effect of noise and resolution
2006
The convolution of tip shape on sample topography can introduce significant inaccuracy in an AFM image, when the tip radius is comparable to the typical dimension of the sample features to be observed. The blind estimation method allows one to obtain information on the AFM tip through an unknown characterizer sample and thus to perform the deconvolution of the tip shape from an image. When applying the blind estimation method to determine the AFM tip shape, some apparently trivial issues relating to the experimental operating parameters must be taken into account. In this paper, the effects of the operating parameters, e.g., sampling intervals (resolution) and instrumental noise, have been …
Infinitely Divisible Distributions
2020
For every n, the normal distribution with expectation μ and variance σ 2 is the nth convolution power of a probability measure (namely of the normal distribution with expectation μ/n and variance σ 2/n). This property is called infinite divisibility and is shared by other probability distributions such as the Poisson distribution and the Gamma distribution. In the first section, we study which probability measures on the real line are infinitely divisible and give an exhaustive description of this class of distributions by means of the Levy–Khinchin formula.
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
2020
Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their a…
Boosting background suppression in the NEXT experiment through Richardson-Lucy deconvolution
2021
The NEXT collaboration: et al.
Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment
2021
[EN] Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulat…
Sensitivity enhancement in pulse EPR distance measurements
2004
Established pulse EPR approaches to the measurement of small dipole-dipole couplings between electron spins rely on constant-time echo experiments to separate relaxational contributions from dipolar time evolution. This requires a compromise between sensitivity and resolution to be made prior to the measurement, so that optimum data are only obtained if the magnitude of the dipole-dipole coupling is known beforehand to a good approximation. Moreover, the whole dipolar evolution function is measured with relatively low sensitivity. These problems are overcome by a variable-time experiment that achieves suppression of the relaxation contribution by reference deconvolution. Theoretical and exp…
On the condition number of the antireflective transform
2010
Abstract Deconvolution problems with a finite observation window require appropriate models of the unknown signal in order to guarantee uniqueness of the solution. For this purpose it has recently been suggested to impose some kind of antireflectivity of the signal. With this constraint, the deconvolution problem can be solved with an appropriate modification of the fast sine transform, provided that the convolution kernel is symmetric. The corresponding transformation is called the antireflective transform. In this work we determine the condition number of the antireflective transform to first order, and use this to show that the so-called reblurring variant of Tikhonov regularization for …
Implicit-explicit methods for a class of nonlinear nonlocal gradient flow equations modelling collective behaviour
2019
Abstract The numerical solution of nonlinear convection-diffusion equations with nonlocal flux by explicit finite difference methods is costly due to the local spatial convolution within the convective numerical flux and the disadvantageous Courant-Friedrichs-Lewy (CFL) condition caused by the diffusion term. More efficient numerical methods are obtained by applying second-order implicit-explicit (IMEX) Runge-Kutta time discretizations to an available explicit scheme for such models in Carrillo et al. (2015) [13] . The resulting IMEX-RK methods require solving nonlinear algebraic systems in every time step. It is proven, for a general number of space dimensions, that this method is well def…
Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and …
2022
Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface mo…